George F Luger ARTIFICIAL INTELLIGENCE 5th edition Structures and Strategies for Complex Problem Solving Machine Learning: Social and Emergent Luger: Artificial Intelligence, 5 th edition. © Pearson Education Limited, Social and Emergent Models of Learning 12.1The Genetic Algorithm 12.2Classifier Systems and Genetic Programming 12.3Artificial Life and Society-Based Learning 12.4Epilogue and References 12.5Exercises 1

Luger: Artificial Intelligence, 5 th edition. © Pearson Education Limited, 2005 A general form of the genetic algorithm. 2

Luger: Artificial Intelligence, 5 th edition. © Pearson Education Limited, 2005 Fig 12.1Use of crossover on two bit strings of length eight. # is “don’t care.” 3

Luger: Artificial Intelligence, 5 th edition. © Pearson Education Limited, 2005 Table 12.1 The gray coded bit patterns for the binary numbers 0, 1, …, 15. 4

Luger: Artificial Intelligence, 5 th edition. © Pearson Education Limited, 2005 Fig 12.2Genetic algorithms visualized as parallel hill climbing, adapted from Holland (1986). 5

Luger: Artificial Intelligence, 5 th edition. © Pearson Education Limited, 2005 Fig 12.3A classifier system interacting with the environment, adapted from Holland (1986). 6

Luger: Artificial Intelligence, 5 th edition. © Pearson Education Limited, 2005 Fig 12.4The random generation of a program to initialize. The circled nodes are from the set of functions. 7

Luger: Artificial Intelligence, 5 th edition. © Pearson Education Limited, 2005 Fig 12.5Two programs, selected on fitness for crossover. Points | from a and b are randomly selected for crossover. 8

Luger: Artificial Intelligence, 5 th edition. © Pearson Education Limited, 2005 Fig 12.6The child programs produced by crossover of the points in Fig

Luger: Artificial Intelligence, 5 th edition. © Pearson Education Limited, 2005 Table 12.3 A set of fitness cases, with planetary data taken from Urey (1952). A is Earth’s semi-major axis of orbit and P is in units of Earth-years. 10

Luger: Artificial Intelligence, 5 th edition. © Pearson Education Limited, 2005 Fig 12.7The target program relating orbit to period for Kepler’s third law. 11

Luger: Artificial Intelligence, 5 th edition. © Pearson Education Limited, 2005 Fig 12.8Members from the initial population of programs to solve the orbital period problem. 12

Luger: Artificial Intelligence, 5 th edition. © Pearson Education Limited,

Luger: Artificial Intelligence, 5 th edition. © Pearson Education Limited, 2005 Fig 12.9The shaded region indicates the set of neighbors for the “game of life.” 14

Luger: Artificial Intelligence, 5 th edition. © Pearson Education Limited, 2005 Fig A set of neighbors generating the “blinking” light phenomenon. Fig What happens to these patterns at the next time cycle? 15

Luger: Artificial Intelligence, 5 th edition. © Pearson Education Limited, 2005 Fig A “glider” moves across the display. Fig a “glider” is “consumed” by another “entity.” 16

Luger: Artificial Intelligence, 5 th edition. © Pearson Education Limited, 2005 Fig Space-time diagrams showing the behavior of two As., discovered by the genetic algorithm on different runs. They employ embedded particles for the nonlocal computation of general emerging patterns seen. Each space-time diagram iterates over a range of time steps, with 1s given as black cells, 0s as white cells; time increases down the page, from Crutchfield and Mitchell (1995). 17

Luger: Artificial Intelligence, 5 th edition. © Pearson Education Limited, 2005 Fig Illustration of a one-dimensional, binary-state, nearest-neighbor cellular automaton with N = 11. Both the lattice and the rule table for updating the lattice are illustrated. The lattice configuration is shown over one time step. The cellular automaton is circular in that the two end values are neighbors. 18

Luger: Artificial Intelligence, 5 th edition. © Pearson Education Limited, 2005 Table 12.4 Catalog of regular domains, particles (domain boundaries), particle velocities (in parentheses), and particle interactions of the space-time behavior of the CA of Figure 11.14a. The notation p ~  x  y means that p is the particle forming the boundary between regular domains  x and  y. 19

Luger: Artificial Intelligence, 5 th edition. © Pearson Education Limited, 2005 Fig Analysis of the emergent logic for density classification of 11.14a. This CA has three domains, six particles, and six particle iterations, as noted in Table The domains have been filtered out using an 18-state nonlinear transducer; adapted from Crutchfield and Mitchell (1994). 20